The analysis of teaching quality evaluation for the college sports dance by convolutional neural network model and deep learning

This study aims to comprehensively analyze and evaluate the quality of college physical dance education using Convolutional Neural Network (CNN) models and deep learning methods. The study introduces a teaching quality evaluation (TQE) model based on one-dimensional CNN, addressing issues such as subjectivity and inconsistent evaluation criteria in traditional assessment methods. By constructing a comprehensive TQE system comprising 24 evaluation indicators, this study innovatively applies deep learning technology to quantitatively assess the quality of physical dance education. This TQE model processes one-dimensional evaluation data by extracting local features through convolutional layers, reducing dimensions via pooling layers, and feeding feature vectors into a classifier through fully connected layers to achieve an overall assessment of teaching quality. Experimental results demonstrate that after 150 iterations of training and validation on the TQE model, convergence is achieved, with mean squared error (MSE) decreasing to 0.0015 and 0.0216 on the training and validation sets, respectively. Comparatively, the TQE model exhibits significantly lower MSE on the training, validation, and test sets compared to the Back-Propagation Neural Network, accompanied by a higher R2 value, indicating superior accuracy and performance in data fitting. Further analysis on robustness, parameter sensitivity, multi-scenario adaptability, and long-term learning capabilities reveals the TQE model's strong resilience and stability in managing noisy data, varying parameter configurations, diverse teaching contexts, and extended time-series data. In practical applications, the TQE model is implemented in physical dance courses at X College to evaluate teaching quality and guide improvement strategies for instructors, resulting in notable enhancements in teaching quality and student satisfaction. In conclusion, this study offers a comprehensive evaluation of university physical dance education quality through a multidimensional assessment system and the application of the 1D-CNN model. It introduces a novel and effective approach to assessing teaching quality, providing a scientific foundation and practical guidance for future educational advancements.


Research background and motivations
In recent years, with the rapid development of artificial intelligence and deep learning technologies, the field of teaching quality evaluation (TQE) has gradually begun to integrate these emerging technologies to enhance the accuracy and efficiency of evaluations.In the context of higher education, particularly regarding the quality of physical dance education, existing research has primarily focused on traditional statistical methods and simple machine learning models.As an essential branch of AI, deep learning models have achieved remarkable outcomes in image recognition, speech processing, natural language processing (NLP), and other areas [1][2][3].However, these methods have certain limitations when dealing with complex, multidimensional TQE indicators, making it difficult to comprehensively and accurately reflect the actual teaching effectiveness.In physical education (PE), the teaching quality of college sports dance is crucial for cultivating students' physical fitness and aesthetic appreciation.However, traditional TQE methods suffer from issues such as strong subjectivity and inconsistent standards, making it difficult to comprehensively and objectively reflect the teaching effectiveness [4][5][6].
With the development of dance teaching, the need for a more comprehensive and objective evaluation of teaching quality has become increasingly prominent.Given the diverse learning demands of students and the complexity of dance skills, the introduction of Convolutional Neural Networks (CNNs) in deep learning offers a powerful solution [7][8][9].CNNs are known for their excellent performance in processing image and time series data, as they can extract abstract features from complex data and are suitable for training on large-scale datasets [10][11][12].Integrating this technology into the TQE of college sports dance can evaluate students' performance more precisely and objectively, providing teachers with targeted suggestions for improvement.
This study aims to explore a CNN-based model for assessing the quality of higher education physical dance instruction and validate its practical application effects.Firstly, in existing research, Hou et al. (2021) discussed the feasibility of integrating machine learning algorithms into TQE [13].They analyzed teaching evaluation data using traditional machine learning models such as Support Vector Machines (SVMs) and Decision Trees, achieving some effectiveness but encountering accuracy issues when handling large-scale and diverse datasets.Secondly, Ranjeeth et al. (2021) studied deep learning-based educational data analysis, proposing the use of Multilayer Perceptron (MLP) models for predicting student performance [14].Their results highlighted the significant advantages of deep learning models in capturing complex patterns within data and improving prediction accuracy.Thirdly, Li et al. (2021) proposed a method using CNNs for educational data classification and assessment, demonstrating the superior performance of CNNs in handling complex educational data [15].However, most of these studies focused on general educational data analysis and prediction, with limited exploration into specific physical dance teaching scenarios.Moreover, traditional TQE methods, such as questionnaire surveys and simple statistical analysis, suffer from subjectivity and limited data processing capabilities, making it difficult to comprehensively and objectively reflect teachers' teaching effectiveness and students' learning experiences.Based on this background, this study selects 24 evaluation indicators, including student satisfaction with courseware, teaching content, methods, attitudes, and effectiveness of teachers, to construct a TQE system for college physical dance.The study introduces a one-dimensional Convolutional Neural Network (1D-CNN) model for evaluation, validating its superior performance in TQE and demonstrating its extensive application prospects in practice.This research outcome contributes to a better understanding of student learning needs among educational professionals, facilitates optimized teaching design, and provides robust support for practical improvements in college physical dance instruction.
This study employs CNNs to assess the quality of college physical dance education, leveraging their capacity to handle complex data and effectively extract features.Traditional TQE methods often rely on manually crafted evaluation criteria and subjective scoring, which can be influenced by evaluator biases, thereby compromising objectivity and accuracy.In contrast, CNNs, as a type of deep learning model, excel in feature extraction and autonomous learning.They can discern crucial features from intricate evaluation data, perform pattern recognition, and classification without explicit rule definitions.In this study, the proposed TQE model based on 1D-CNN extracts local features from evaluation data using convolutional layers.It simplifies model complexity while retaining essential information through pooling layers and maps abstract features to the final evaluation output via fully connected layers.Compared to traditional evaluation methods and other deep learning models, 1D-CNN demonstrates superior performance in handling sequential data, particularly suited for processing time-series and textual data relevant to multidimensional assessment in physical dance TQE.Moreover, the research methodology in this study showcases the advantages of the 1D-CNN model over traditional Backpropagation Neural Networks (BPNN) in experimental results: the 1D-CNN model achieves higher accuracy and enhanced robustness on both training and test sets, especially in adapting to prolonged learning effects and diverse teaching scenarios, thus demonstrating significant performance superiority.These findings not only validate the efficacy of 1D-CNN in educational assessment but also establish a reliable methodological framework and technical pathway for future studies in similar domains.Therefore, the primary contribution of this study is the proposal and validation of the TQE model based on 1D-CNN.This model automates learning and analysis of multidimensional TQE data, improving assessment objectivity and accuracy.It also establishes a scientific foundation and technological framework for managing and applying practical solutions in university physical dance education.

Research objectives
The main objective of this study is to investigate and develop a TQE model for college sports dance using CNNs.Specifically, it aims to construct a comprehensive and systematic TQE system.The selected evaluation indicators are trained and tested using the CNN model to extract local characteristics of teaching quality and implement the TQE of college sports dance.Through comparative experiments and TQE experiments applied to two sports dance teachers, the proposed TQE model's ability to accurately evaluate S. Guo et al. teaching quality in actual scenarios is verified.Additionally, the model's practicability and effectiveness are compared with real-world values.This study aims to better meet students' learning requirements and enhance teaching quality by optimizing teaching design and offering practical support for teaching improvement.

Literature review
In the relevant research on TQE, De-kun et al. (2021) designed and applied a new mobile intelligent evaluation algorithm and implemented a mobile intelligent bidirectional long short-term memory model to complete the sequence annotation of sports tasks.Additionally, they used a genetic algorithm to calculate the optimal solution of model parameters, realizing the design of a mobile intelligent sports evaluation system.The experimental results showed that this algorithm had higher evaluation accuracy and better stability than traditional mobile intelligent sports evaluation algorithms [16].Adams and McLennan (2021) proposed a triple heuristic framework for primary TQE, emphasizing the role of educational ontology in cultivating students and teachers and highlighting the importance of identifying, implementing, and understanding teaching practices [17].Brownell et al. (2020) discussed the current research status and future research directions in the field of special education through a comprehensive literature review and comparison, aiming to improve education quality and develop a comprehensive research agenda [18].Litke et al. (2021) compared the differences between three math-focused observation frameworks in measuring teaching quality and considered the advantages of using a comprehensive index list.They found that both the mathematical professional and comprehensive frameworks had their strengths and weaknesses, providing beneficial insights for measuring teaching quality [19].
In addition, many scholars have applied AI technologies, such as deep learning, to teaching.Kastrati et al. (2021) summarized 92 relevant studies on sentiment analysis using NLP, deep learning, and machine learning solutions in education over the past five years through a system mapping study.They highlighted key trends and challenges in the field, such as rapid development, growth in deep learning applications, standardization, and sentiment detection [20].Zhu et al. (2023) introduced the flipped classroom model supported by deep learning and music to significantly improve students' motor skills, physical fitness, deep learning, and autonomous learning ability in PE, providing an experimental reference for subsequent research on adolescent education [21].LecturePlus is a teaching mode that combines lectures with group learning activities.Hashim et al. (2023) evaluated the effectiveness of LecturePlus in medical education and found that it promoted the application of knowledge and improved learning outcomes in medical education [22].Lin and Chen (2020) developed a programming learning system that includes augmented reality (AR) technology, learning theory, and a deep learning recommendation system to stimulate the potential of non-professional students by providing them with learning opportunities.The results indicated that students who used the deep learning-recommended AR system performed better in terms of academic performance and computational thinking ability [23].Due to its powerful feature extraction capabilities, the CNN model has been widely applied in TQE.Xu et al. (2023) utilized the CNN model in their research to analyze classroom teaching videos, evaluating teachers' teaching performance and students' engagement by extracting key frames and features from the videos [24].Their study demonstrated that the CNN model effectively captures critical details in the teaching process, thereby providing accurate evaluation results.Additionally, deep learning models are not limited to video analysis but are also used in processing other forms of educational data.For instance, Yoo et al. (2023) proposed a model based on Long Short-Term Memory (LSTM) network to analyze students' learning behavior data, predicting their academic performance and teaching effectiveness [25].Their research indicated that deep learning models exhibit significant advantages in handling time-series data, accurately capturing the dynamic changes in students' learning behaviors.
In the field of educational assessment, an increasing number of studies are employing deep learning models for evaluating teaching quality, particularly using CNNs and their variants to process educational data.The 1D-CNN-based TQE model proposed here holds significant innovation and application value in this context.Firstly, previous research has extensively explored the methods and effects of using CNN models for educational data analysis.For example, Poudyal et al. (2022) utilized CNNs to predict student learning performance by extracting local patterns from textual data to evaluate student progress [26].Their study revealed the potential of CNNs in handling multidimensional educational data, which aligns with the 1D-CNN-based TQE model proposed in this study, both focusing on extracting key features from sequential data.Secondly, numerous empirical studies support the application of deep learning models in educational assessment.For instance, Tsiakmaki et al. (2020) explored a deep neural network-based student performance prediction model, enhancing prediction accuracy through multi-level feature extraction and abstraction [27].Their work emphasized the advantages of deep learning in analyzing complex educational data, resonating with the objective of the 1D-CNN model to evaluate teaching quality presented in this study.Additionally, some research focuses on utilizing deep learning technology to improve teaching effectiveness.For example, Liu et al. (2022) examined how CNNs could enhance course recommendation systems on online education platforms [28].Their study, from the perspective of technical improvement, demonstrated the diversity and feasibility of CNN applications in the educational field, providing an empirical foundation for the application of the 1D-CNN model in evaluating teaching quality.The BPNN is another deep learning model commonly used for TQE.Compared to CNNs, BPNNs perform well in handling non-image data and are particularly suitable for fields with abstract evaluation indicators.Wu (2024) applied a BPNN model to analyze teaching evaluation data of university instructors, offering comprehensive TQE results through the modeling of multidimensional evaluation indicators [29].Their research found that the BPNN model achieved high accuracy in capturing the multidimensional features of teaching quality.However, despite its applications, BPNN has several limitations.First, BPNN tends to overfit when handling large-scale complex data, leading to unstable evaluation results.Second, BPNN is highly sensitive to parameter settings, and different parameter combinations significantly affect model performance, requiring extensive parameter tuning.Additionally, BPNN is less stable than CNN models when processing long time-series data and noisy data [30].In summary, the academic community is increasingly focusing on the application of deep learning in educational assessment, particularly in processing S. Guo et al. educational data using CNNs and their variants.The 1D-CNN model proposed in this study not only innovates upon traditional evaluation methods but also enriches the research paradigm of educational assessment models at both theoretical and empirical levels.This provides new perspectives and methods for TQE.
Through the above research, it can be found that the current evaluation of teaching quality includes the application of mobile intelligent evaluation algorithms in PE tasks, the proposal of primary TQE frameworks, and the research and comparison of observation frameworks focusing on special teacher education and mathematics.Research on the application of deep learning in teaching has involved sentiment analysis using deep learning, NLP, and machine learning solutions, as well as the implementation of deep learning and music-supported flipped classroom models in PE.However, few studies have focused on the specific analysis of particular curricula.Therefore, this study employs deep learning technology to discuss its application in the TQE of college sports dance.

Construction of the TQE system
To evaluate the teaching quality of college sports dance thoughtfully and systematically, it is necessary to build a comprehensive TQE system.The system should cover multiple aspects and aim to evaluate teachers' teaching content, methods, attitudes, and effects [31][32][33].Additionally, in the current context of "AI + education", sports dance teachers occasionally conduct online teaching, which requires the use of courseware and other materials.This study aims to construct a comprehensive evaluation system for the teaching quality of college sports dance courses by selecting 24 specific evaluation indicators.These indicators cover multiple dimensions, including teaching content, teaching methods, teaching attitude, teaching effectiveness, and student satisfaction with course materials.The goal is to provide a scientific and systematic evaluation standard for the teaching quality of college sports dance courses [34][35][36].Firstly, regarding the evaluation of teaching content, Vinnervik (2023) highlighted that a reasonable content structure and clear teaching objectives are key to effective teaching [37].The evaluation indicators in this study include the rationality of content structure (A1), clarity in delivering teaching content (A2), richness of content (A3), and clarity of teaching objectives (A4).These indicators comprehensively reflect the teacher's performance in course content design and delivery, ensuring the systematization and logicality of teaching content, thereby enhancing students' learning outcomes.Secondly, the evaluation of teaching methods is crucial in assessing teaching quality.Zhan et al. (2021) emphasized the importance of diversified teaching strategies and teacher-student interaction, which significantly impact students' learning interest and engagement [38].The evaluation indicators for teaching methods selected in this study include the emphasis on stimulating students' learning interest (A5), thoroughness of teacher explanations (A6), teacher's time management ability (A7), effective assignment arrangement (A8), the support for students' individual development (A9), and diversified teaching strategies (A10).These indicators aim to comprehensively evaluate the diversity and effectiveness of teachers' teaching methods, promoting more active student participation in the learning process.Regarding the evaluation of teaching attitude, Getie (2020) noted that a teacher's teaching attitude directly affects students' learning experience and outcomes [39].The evaluation indicators for teaching attitude in this study include the quality of learning resources provided by the teacher (A13), seriousness and enthusiasm in teaching (A14), patience in answering questions (A15), and timely correction of assignments (A16).These indicators comprehensively reflect the teacher's teaching attitude and sense of responsibility, ensuring that the teacher maintains a positive and professional attitude throughout the teaching process, thereby enhancing students' learning satisfaction.Furthermore, the evaluation of teaching effectiveness is a core aspect of TQE.The evaluation indicators for teaching effectiveness in this study include enhancing students' learning interest (A18), enriching students' knowledge (A19), promoting students' comprehensive development (A20), and ensuring students effectively grasp key movements (A21).These indicators objectively reflect the teacher's teaching effectiveness, ensuring that students not only acquire knowledge and skills but also develop their personal abilities and qualities comprehensively during the learning process.Finally, student satisfaction with course materials is an important aspect of evaluating the teaching quality of college sports dance courses.The evaluation indicators for course material satisfaction in this study include the teaching effectiveness of the course materials (A22), the technical quality of the course materials (A23), and the artistic quality of the course materials (A24).These indicators aim to comprehensively evaluate the auxiliary role of course materials in teaching, ensuring that the course materials effectively support the teacher's teaching activities and enhance the students' learning experience.In conclusion, the evaluation system for college sports dance teaching quality constructed in this study, through the comprehensive and systematic selection of 24 specific evaluation indicators, assesses teaching quality from multiple dimensions.This provides a scientific and accurate evaluation standard for the teaching quality of college sports dance courses.This evaluation system not only helps to gain an in-depth understanding of various aspects of the teaching process but also provides scientific evidence for teachers to improve their teaching design and methods, ultimately enhancing the teaching quality and students' learning outcomes in college sports dance courses.The specific evaluation indicators are shown in Table 1 [40][41][42].
Through the above evaluation system, this study comprehensively measures multiple dimensions of the teaching quality of college sports dance, offering diverse and specific labels for subsequent DL-based models.This evaluation system contributes to achieving a deeper understanding of various aspects of teaching, thereby providing more accurate and comprehensive evaluation results.

Introduction of the CNN model in deep learning
When assessing teaching quality, the 24 independent evaluation indicators form a multidimensional data space where each indicator may interact or influence the final assessment outcome.Traditional classifiers such as SVM or Random Forests (RF) are typically suited for datasets where features are relatively independent and linearly separable.However, in TQE, these indicators are often not completely independent but may exhibit complex correlations and dependencies.CNNs, as a type of deep learning model, possess the capability to extract local features from both image and sequential data, enabling them to effectively capture spatial and temporal correlations within the data.Through convolution operations, CNNs can automatically learn and extract crucial features from the data without requiring intricate manual feature engineering.This ability is particularly significant in the context of TQE, where evaluation indicators often exhibit complex spatial and temporal structures.For instance, different teaching methods may have varying impacts over different time periods, and CNNs can effectively capture these key patterns and features through convolution and pooling layers.Therefore, compared to traditional classifiers, the use of CNNs allows for a more accurate and comprehensive understanding and evaluation of multidimensional teaching quality data.This enhances the model's predictive and generalization capabilities, providing education administrators and decision-makers with more reliable and effective decision-making criteria.
CNN is a type of deep learning model primarily used in image recognition, computer vision, and image processing [43,44].The design of CNN is inspired by the structure of the visual cortex in biology, with its main feature being the extraction of local image features through convolutional and pooling layers, followed by high-level abstraction and classification using the fully connected layer [45][46][47][48].A basic CNN consists of input, convolutional, pooling, fully connected, and output layers, as illustrated in Fig. 1.
After the input image undergoes convolution with the convolution kernel, convolutional layer 1 is obtained.Each pixel of the feature maps in convolutional layer 1 is sampled to produce pooling layer 1. Subsequently, convolutional layer 3 and pooling layer 4 are obtained after another round of convolution and sampling from pooling layer 1.Finally, the feature maps of pooling layer 4 are connected to the output layer through the fully connected layer, forming a feature vector.The specific operation of the convolutional layer involves convolving the convolution kernel with the feature information (FI) from the previous layer, incorporating the bias term, and applying a nonlinear activation function to obtain the FI of the new layer [49][50][51][52].The operational process of the convolutional layer is represented by Equation (1).
In Equation ( 1), l refers to the number of convolutional layers; f means the nonlinear activation function; q l j indicates the jth FI in convolutional layer l; u l ij denotes the convolution kernel; A j signifies the set of input FI; φ l j represents the bias term.After passing through the convolutional layer, the pooling layer is obtained, and its workflow resembles that of the convolutional layer.In the pooling layer, segments of the FI from the previous layer are down-sampled, as described in Equation (2).
Here, w l j implies the jth FI in pooling layer n; t l j indicates the sampled value; down(*) represents the subsampling function.The fully connected layer integrates the diverse feature maps extracted from the convolutional layers into an eigenvector, which is then input into the classifier for classification.
1D-CNN is a variant of CNN specifically designed to handle 1D sequential data, such as signal data, time series, or text data [53][54][55].Unlike traditional 2D convolution used in image processing, 1D-CNN is primarily employed to learn and extract local patterns in sequential data [56][57][58].1D-CNN operates similarly to regular CNNs, extracting local features through convolution operations on input sequences and reducing sequence length through pooling operations [59,60]].

TQE model based on 1D-CNN
In the constructed model, the input layer receives 1D evaluation data, which can include assessments of teaching attitude, content, effectiveness, methods, and courseware satisfaction.Each dimension of evaluation data forms a sequence, constituting an input sequence.The convolutional layer extracts local features from this input sequence by sliding one or more one-dimensional convolution kernels.This process enables the network to capture key patterns and significant features within the evaluation data, enhancing the model's ability to understand and learn from it.The pooling layer employs maximum pooling operations to reduce the output dimension of the convolutional layer by retaining the maximum value within each pooling window.This step aims to decrease model complexity, enhance computational efficiency, and preserve critical information essential for focusing the model on significant features.Subsequently, the fully connected layer transforms the output from the pooling layer into 1D eigenvectors, facilitating further classification or regression tasks.This layer maps the abstract features learned by the CNN to generate a comprehensive evaluation of teaching quality across all dimensions, as illustrated in Fig. 2.
Fig. 2 illustrates the input layer of the 1D-CNN model, receiving multidimensional evaluation data from students on physical education dance courses.These evaluations encompass teaching content, methods, attitudes, and effectiveness, preprocessed to fit the model's input format.The convolutional layers then extract local features from the evaluation data through convolution operations, emphasizing key teaching elements.Subsequently, the pooling layers reduce the output dimensionality to decrease parameters and highlight critical features.In the fully connected layers, the model integrates these extracted features to form a comprehensive assessment of teaching quality.Finally, the output layer translates the information into evaluations of teaching effectiveness, directly correlating with specific indicators of TQE.

Model evaluation indicator
Mean squared error (MSE) and coefficient of determination R 2 are used to measure the model's performance [61,62].Among them, MSE measures the difference between the predicted and the real values and is calculated as Equation ( 3): In Equation ( 3), y i represents the real value, ŷi refers to the predicted value, and N stands for sample size.A smaller MSE indicates higher prediction accuracy of the model on the sample data.R 2 measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s).It is calculated by Equation ( 4): y is the mean of the observed data.R 2 ranges from 0 to 1, with values closer to 1 indicating a better fit of the model to the data and thus a better predictive effect.

Datasets collection
The raw data for this study originates from a questionnaire survey conducted among college students enrolled in physical education dance courses.The questionnaire items are designed based on the TQE system developed for college physical education dance instructors, with each evaluation criterion scored on a scale of 0-100.A total of 1002 responses are collected using the Questionnaire Star tool via online platforms.These survey data are standardized and used as the raw dataset for the models.The questionnaire comprehensively evaluates the teaching quality of physical education dance courses, including specific assessments of student satisfaction with course materials, teaching content, teaching methods, teaching attitudes, and teaching effectiveness.For instance, the questionnaire includes questions that assess the rationality of teaching content structure, clarity in delivering teaching content, richness of content, and clarity of teaching objectives.Additionally, it covers evaluations of teachers' abilities to stimulate student interest in class, the thoroughness of explanations, time management skills, effectiveness of homework arrangements, and diversity of teaching strategies.Regarding teaching attitudes, the survey assesses the quality of learning resources provided by teachers, the seriousness and enthusiasm of teaching attitudes, patience in answering questions, and promptness of homework corrections.Evaluation of teaching effectiveness involves aspects such as enhancing student interest in learning, the breadth of knowledge gained by students, comprehensive development of student personalities, and effective mastery of dance techniques.Lastly, it includes evaluations of satisfaction with the effectiveness, technicality, and artistic quality of course materials.Through these questions, the aim is to gather feedback from students on the teaching quality of physical education dance courses across multiple dimensions.This approach facilitates the construction of a comprehensive and systematic teaching quality evaluation system, enabling a scientific and accurate assessment of teaching quality.Such questionnaire design helps to gain insights into various aspects of the teaching process and provides a scientific basis for teachers to improve teaching designs and methods, ultimately enhancing teaching quality and student learning outcomes.
To mitigate the influence of outliers on the model results, the data undergoes standardization, as shown in Equation ( 5).
S. Guo et al.
In Equation ( 5), x denotes the original values; x refers to the mean; σ represents the standard deviation.The standardized dataset serves as the model's input.Following a 6:2:2 ratio, the dataset is partitioned into training, validation, and test sets.

Experimental environment and parameter settings
The experimental environment and parameter settings are detailed in Table 2.

Performance evaluation
(1) Analysis of prediction results of the 1D-CNN model The training and validation sets undergo 150 iterations, and the variations of MSE are depicted in Fig. 3.In Fig. 3, as iterations increase, MSE gradually decreases for both the training and validation sets, indicating a convergence trend.This suggests that the model progressively learns patterns and features from the data during training, improving its fitting capability.Eventually, MSE stabilizes at 0.0015 for the training set and 0.0216 for the validation set, both of which are relatively small.This indicates that data standardization accelerates gradient descent, leading to efficient model training and faster convergence.
The fitting curve of the test set is depicted in Fig. 4. In Fig. 4, the horizontal axis represents the sample's real values, while the vertical axis displays the model's predicted results and the mean value of the output vector.Fig. 4 illustrates that the R 2 of the 1D-CNN model is 0.877 on the test set, indicating a high degree of model fit.
The 1D-CNN model is compared with a BPNN.The BPNN adopts a 4-layer network structure with a Sigmoid activation function, utilizes the Adam optimizer with a learning rate set to 0.001, and employs MSE as the output layer's activation function.The comparative experimental results are depicted in Fig. 5.
Fig. 5 shows that the MSE values of 1D-CNN are significantly lower than those of BPNN on the training, validation, and test sets, which are 0.0015, 0.0216, and 0.0217, respectively, while those of BPNN are 0.0064, 0.0457, and 0.0458, respectively.Additionally, the R 2 scores of 1D-CNN on the training, validation, and test sets markedly exceed those of BPNN, which are 0.91, 0.87, and 0.88, respectively, while BPNN scores are 0.82, 0.77, and 0.76, respectively.These results indicate that the 1D-CNN model accurately fits the data and performs better than BPNN.
Overall, the college physical education dance TQE model proposed in this study, based on 1D-CNN, demonstrates significant advantages across multiple dimensions.Firstly, 1D-CNN efficiently captures local features and patterns in input data through convolution operations, thereby achieving high efficiency in feature extraction.Compared to traditional BPNNs, 1D-CNN converges faster during training and exhibits significantly lower MSE across multiple datasets, indicating a better fit to the data.On the training and validation sets, 1D-CNN achieved MSE values of 0.0015 and 0.0216, respectively, while BPNN's MSE values are 0.0064 and 0.0457.This substantial difference illustrates that 1D-CNN more accurately reflects the actual data trends in both training and validation phases, demonstrating stronger generalization ability.Moreover, the fitting curve on the test set shows that 1D-CNN attains an R 2 value of 0.877, highlighting its high goodness of fit and further validating its efficiency and stability.Additionally, the evaluation of actual teaching quality for two physical education dance instructors indicated that the maximum difference between predicted and actual values using the 1D-CNN model is only 1.15 %.This high accuracy in practical application underscores the model's effectiveness.In contrast, BPNN, while capable of conducting TQE in the same test environment, exhibited inferior MSE and R 2 values compared to 1D-CNN, indicating lower predictive precision and fitting performance.The advantage of the 1D-CNN model lies in its deep learning algorithm's ability to handle large volumes of data efficiently within a short timeframe and extract effective features.Data normalization accelerates gradient descent, enhancing the model's convergence speed and stability during training.Ultimately,     the 1D-CNN-based college physical education dance TQE model proposed in this study offers an innovative evaluation approach for educators.It helps them better understand students' learning needs, optimize teaching designs, and provides robust support for practical teaching improvements.The robustness analysis results of 1D-CNN and BPNN are shown in Table 3: it is found that as the noise level in the dataset increased, the performance of both models declined, but the decrease in performance of the 1D-CNN model is significantly smaller than that of the BPNN model.Specifically, even at high noise levels, the MSE and R 2 values of 1D-CNN remained significantly better than those of BPNN, indicating that 1D-CNN exhibits better robustness in handling noisy data.
The parameter sensitivity analysis results are shown in Table 4: it is observed that the performance of the 1D-CNN model varied relatively little under different parameter settings, indicating insensitivity to hyperparameters.In contrast, the performance of the BPNN model is more sensitive to parameter selection, particularly to slight changes in learning rate, which could lead to significant performance declines.This further confirms the flexibility and stability of the 1D-CNN model in hyperparameter selection.
Data from various teaching styles and course types are collected to compare the adaptability of 1D-CNN and BPNN.The results of the multi-scenario adaptability analysis are shown in Table 5.The findings indicate that the 1D-CNN model maintains lower MSE and higher R 2 values across different types of teaching scenarios, demonstrating good adaptability.Whether in theoretical classes, practical classes, or mixed classes, the 1D-CNN model accurately evaluates teaching quality.In contrast, the BPNN model shows less stable performance across different types of teaching scenarios, particularly exhibiting noticeable performance declines in theoretical class scenarios.
Evaluation datasets with different time spans are constructed to test the model's long-term learning capability.The results of the long-term learning capability analysis are presented in Table 6: in the analysis of long-term learning capability, the 1D-CNN model demonstrated excellent performance.Even when handling data with a time span of 12 months, the 1D-CNN model maintained low MSE and R 2 values close to 0.9, indicating its ability to effectively capture long-term trends and patterns.In contrast, the performance of the BPNN model declined when dealing with long-term data, especially evident in cases with longer time spans, where its predictive accuracy and fitting goodness are inferior to those of the 1D-CNN model.
(2) Analysis of the practical application effect of the TQE model In practical application, the TQE model is first used to evaluate the teaching quality of sports dance courses at the current stage, and adjustments are made based on identified improvement indicators.After one semester, the teaching quality is reassessed.By comparing the real and predicted values from the two evaluation results, the actual effectiveness of the proposed evaluation model can be determined.
Selecting students enrolled in sports dance courses at X College as the research subjects, a total of 4 teachers and 198 students participated in the survey.The teaching quality variations over different time periods for Teachers A and B are illustrated in Fig. 6.
Fig. 6 (a) illustrates that when the TQE model evaluates Teachers A and B, the real and predicted values of most elements closely align, indicating the model's accuracy in predicting teaching quality.Upon comparing the two teachers, it is evident that Teacher A shows lower teaching effectiveness and courseware satisfaction, whereas Teacher B exhibits deficiencies in teaching content and effectiveness.These aspects require individual improvement strategies.Based on the initial evaluation results, specific teaching improvement strategies are suggested for Teachers A and B. Teachers can then adjust their teaching practices accordingly to enhance the quality of online teaching and improve students' online learning outcomes.In Fig. 6(b), the TQE outcomes for both Teachers A and B closely approximate the real values, with a maximum difference of only 1.15 %.This demonstrates the 1D-CNN-based model's capability to effectively assess the teaching quality of college sports dance.Targeted teaching improvement strategies have been observed to positively impact both teachers' teaching quality, thereby increasing student satisfaction with their instruction.In practical application, educational administrators can further analyze the effectiveness of teaching improvements by comparing the results of the two evaluations.This analysis enables administrators to provide more tailored guidance for subsequent teaching enhancements.

Discussion
This study employs a 1D-CNN model-based approach to assess teaching quality, yielding significant results.However, alongside the 1D-CNN model, other machine learning methods in the educational domain also deserve attention and comparison.For instance, the BPNN, though not achieving comparable performance to the 1D-CNN in this study, may perform exceptionally well under specific circumstances.BPNN's advantages lie in its simple network structure and relatively fewer parameter settings, potentially making it more competitive in scenarios with small datasets or requiring rapid training.Moreover, classical machine learning algorithms such as Decision Trees, SVMs, and ensemble methods like RF are widely used in TQE.These methods offer specific advantages in handling diverse data types or scenarios requiring strong interpretability.For example, decision trees provide intuitive decision processes and feature importance insights, while SVM excels in managing high-dimensional data and complex nonlinear relationships.When comparing the performance and applicability of these methods, the 1D-CNN model demonstrates outstanding capability in handling sequential data and long-term learning, particularly excelling in extracting multidimensional evaluation features of teaching quality as described in this study.Nevertheless, researchers in practical applications should select the most suitable model based on specific problems and data characteristics to achieve optimal evaluation effectiveness and predictive accuracy.In conclusion, this study underscores the effectiveness and advantages of the TQE method based on the 1D-CNN model, while also acknowledging the potential and applicability of other machine learning methods in specialized contexts.Future research could explore hybrid models or combinations of different methods to enhance the comprehensiveness and accuracy of educational assessment.The research on the 1D-CNN model in various fields consistently demonstrates its superior performance.For instance, in the medical domain, Moitra 2021) implemented a method using the 1D-CNN model for predicting haze concentration.By inputting past 24-h haze concentration data, they successfully predicted hourly haze concentration levels with over 95 % accuracy, providing significant support for haze prediction research [65].In conclusion, an increasing number of studies confirm the efficacy of the 1D-CNN model in prediction tasks across various domains.The above study illustrates that the 1D-CNN model demonstrates superior performance across various prediction tasks.However, it is important to acknowledge certain limitations of the 1D-CNN model.For instance, compared to traditional neural network models, 1D-CNN may face challenges when handling complex time-series data, particularly in scenarios involving large datasets or high levels of noise.Additionally, while the training process of the 1D-CNN model is relatively stable, fine-tuning of hyperparameters remains essential to ensure optimal performance.To further validate the effectiveness of the 1D-CNN model, comparative analyses were conducted with other commonly used deep learning models.For example, the BPNN, a classic neural network model widely applied across various fields, performs adequately in simpler prediction tasks but often lags behind 1D-CNN in handling complex datasets.Experimental results indicate that across robustness, parameter sensitivity, and multi-scenario adaptability analyses, 1D-CNN consistently outperforms BPNN.This underscores 1D-CNN's superior capabilities in managing multidimensional complex data, noise reduction, and adapting to diverse application scenarios.In summary, this study comprehensively evaluated college physical education dance teaching quality through the development of a multidimensional assessment system and the application of the 1D-CNN model.It highlights the potential and advantages of using 1D-CNN in TQE.Furthermore, through comparisons with BPNN and other models, it reaffirms the outstanding performance of 1D-CNN in handling complex data and various application scenarios.Future research could explore more diverse datasets and advanced deep   learning models to further enhance the accuracy and comprehensiveness of TQE.

Research contribution
This study contributes significantly by introducing a 1D-CNN for assessing the teaching quality of college physical education dance.Firstly, the study designs a TQE framework that includes 24 evaluation indicators, such as student satisfaction with courseware, teacher's instructional content, methods, attitudes, and effectiveness.Additionally, it develops an evaluation model based on 1D-CNN.Secondly, through comparison with the traditional BPNN model, the study demonstrates that the 1D-CNN model outperforms BPNN in terms of training, validation, and testing effectiveness, exhibiting higher fitting and prediction accuracy.Finally, the effectiveness of the 1D-CNN model in evaluating physical education dance teaching quality is validated in practical applications, with results showing a maximum difference of only 1.15 % from actual assessment values.This confirms the model's ability to accurately assess teaching quality, providing educational managers with a scientific assessment tool and improvement strategies.

Future works and research limitations
While this study has yielded positive research outcomes, there are still limitations and areas for further improvement.Firstly, the sample dataset and study subjects were limited to specific college physical education dance courses, which restricts sample coverage and diversity.Enhancing the universality and generalization capability of the model requires expanding the scope of samples.Secondly, although the 1D-CNN model performed well in most scenarios, further optimization and validation are needed for handling long-term learning effects and adapting to specific teaching scenarios.Future work could focus on scaling up and diversifying the  dataset, exploring more complex model structures, and integrating other deep learning techniques such as attention mechanisms to enhance the overall performance and stability of the evaluation model.Additionally, interdisciplinary analysis of more educational data could be considered to delve deeper into the application potential of TQE models across different disciplines and educational stages.This approach would provide more scientifically grounded support and recommendations for educational practices and policymaking.

Fig. 4 .
Fig. 4. Fitting curve of the real and predicted values of the 1D-CNN model within the test set.

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and Mandal et al. (2020) developed a 1D-CNN model for the automatic staging and grading of Non-Small Cell Lung Cancer (NSCLC).Using the NSCLC Radiogenomics Collection dataset and a mixed feature detection and extraction model, they input extracted features and clinical information into the 1D-CNN model.Studies have shown that this proposed model outperformed other machine learning methods in predicting NSCLC, achieving higher accuracy, thus highlighting its potential effectiveness in automatic NSCLC staging and grading [63].In the realm of network security, Qazi et al. (2022) employed the 1D-CNN model to propose a deep learning-based architecture for network intrusion detection.Their approach successfully detected four different types of network intrusions, including DoS Hulk, DDoS, DoS Goldeneye, and PortScan.Experimental results demonstrated an accuracy rate of 98.96 % [64].In meteorology, Zhang et al. (

Fig. 6 .
Fig. 6.Teaching quality variations over different time periods for two teachers ((a) results of the first TQE, (b) results of the second TQE).

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Table 1
Evaluation indicator of college sports dance teaching quality.

Table 2
Experimental environment and parameter settings.

Table 3
Robustness analysis results of 1D-CNN and BPNN.

Table 4
Parameter sensitivity analysis results.

Table 5
Multi-scenario adaptability analysis results.

Table 6
Long-term learning capability analysis results.